The Resume & Applying

What actually happens to your resume

Before you write a word, understand the gauntlet your resume runs. When you click "apply," it doesn't go straight to a hiring manager who reads it carefully over coffee. It hits two filters in sequence, and you have to pass both:

The first reader is software — an applicant tracking system (ATS). It parses your file into fields, scans for keywords from the job description, and either filters you out or ranks you for a human. The second reader is a busy recruiter who spends roughly six to eight seconds on the first pass, scanning for: the right title, the right skills, a few numbers that signal impact, and recency. If either filter trips, you're out — no matter how good you'd be at the job.

Resume structure for a data engineer

Keep it to one page. You're early-career or career-changing; a second page dilutes your strongest material and signals you don't know what matters. Use this order, and notice how it differs for a career-changer:

CONTACT       Name · City · email · phone · LinkedIn URL · GitHub URL
              (no full street address; no photo)

SUMMARY       2-3 lines. Who you are + what you do + what you're targeting.
              "Career-changing data engineer with a production-grade ELT
               platform (dbt, Airflow, Snowflake). Former [X] who now builds
               reliable, tested data pipelines."

SKILLS        The literal tools, grouped. THIS is where ATS keywords live.
              Languages: SQL, Python
              Transform/Orchestrate: dbt, Airflow, Dagster
              Cloud/Warehouse: AWS, Snowflake, BigQuery
              Infra: Docker, Git, Linux, Terraform

PROJECTS      ← For a career-changer, put this HIGH (right after Skills).
              Your capstone goes here. This is your proof you can do the job.

EXPERIENCE    Your work history, with impact bullets. Even non-data jobs count
              if you frame the transferable parts (data, automation, scale).

EDUCATION     Degree(s), bootcamp/certs. One or two lines. Keep it last.

Why a dedicated Skills section matters so much: it's the densest, most scannable place for the exact tool names a recruiter and an ATS are hunting for. Don't make them infer "they probably know dbt" from a buried sentence — list it plainly. We'll come back to which keywords to include in the ATS and tailoring sections.

Impact-driven bullets

This is where most resumes are weakest and where you can stand out fastest. A weak bullet describes a task ("responsible for managing reports"). A strong bullet proves an outcome. Use this formula on every single bullet:

Strong verbs to open with: built, automated, designed, reduced, migrated, optimized, scaled, shipped, eliminated, cut. Avoid limp openers like "responsible for," "helped with," "worked on." Here's the transformation in practice:

Featuring your projects when you lack DE experience

Here is the career-changer's secret weapon. You may not have a "Data Engineer" job on your resume yet — but you have something most applicants don't: a real, working, documented platform from your capstone. Treat it exactly like a job. Give it a name, a one-line description of what it does, and impact bullets using the same formula.

For each project, answer three things: what it does, the stack, and what you personally handled. Here's the shape:

Retail Analytics Platform — end-to-end ELT pipeline    [GitHub] [demo]
Python · dbt · Airflow · Snowflake · Docker · Terraform

• Built an end-to-end ELT platform ingesting ~2M rows/day from 3 source
  APIs into Snowflake, transformed by a 30-model dbt project with tests.
• Orchestrated daily runs in Airflow with retries, SLAs, and Slack alerts;
  added data-quality tests that block bad data from reaching dashboards.
• Containerized the full stack with Docker and provisioned infra with
  Terraform, so the whole platform spins up from one command.

Pair every project with a working GitHub link and, if you can, a short README or demo. The link turns a claim into evidence — and a clickable repo is exactly what a curious hiring manager wants on that six-second pass.

Beating the ATS

The applicant tracking system parses your file and matches it against the job description. Most early rejections aren't human decisions — they're parsing failures and missing keywords. You beat the ATS by being boring and explicit:

Tailoring per role

A single generic resume is the slowest path to a job. You don't need a from-scratch rewrite each time — but you should tailor at least the summary and skills to each posting. Read the JD, note its top 5–8 hard skills and the phrases it repeats, and adjust:

• Summary: echo the role's framing. If they want someone "building reliable batch pipelines on AWS," your summary should say roughly that — in your own words, truthfully.
• Skills: reorder so the tools they emphasize appear first, and make sure every JD keyword you legitimately have is present.
• Bullets: if you have several, surface the ones most relevant to this role toward the top.

Application strategy

Where you apply matters more than how many times you apply. The single highest-yield channel, by a wide margin, is a referral — a current employee passing your resume to the recruiter. Referrals skip much of the ATS lottery and arrive with built-in credibility. Here's how channels stack up:

How to get referrals (you have more reach than you think):

• List people you know — former colleagues, classmates, bootcamp peers, friends — and check who works at companies you'd like.
• Reconnect before you ask. A genuine catch-up beats a cold "can you refer me."
• Make the ask easy: name the specific role and link, share your capstone, and offer your tailored resume. "Would you be comfortable referring me for this role? Here's the link and my resume — no worries either way."
• Use LinkedIn (Chapter 02) to find second-degree connections at target companies, and engage authentically before reaching out.

✓ Check yourself

• Can you name the two filters every resume must pass, and what each one looks for?
• Does every bullet on your resume start with a strong verb and contain a number?
• Is your capstone written up as a Projects entry with a stack line and impact bullets?
• For a target JD, does every hard skill you have appear verbatim on your resume?
• Do you have at least one referral attempt in progress this week, logged in your funnel sheet?

1. Responsible for moving data from CSV files into a database.
2. Worked on reports to help the marketing team.
3. Used Python scripts to do some automation.

1. Built a Python loader that ingests 20+ daily CSV feeds into Postgres
   with validation, replacing a manual import that took ~5 hours/week.

2. Automated a weekly marketing-metrics pipeline in SQL + dbt, cutting
   report turnaround from 2 days to under 1 hour and removing 3 manual steps.

3. Automated 4 recurring data-prep tasks with Python scripts on a schedule,
   eliminating ~8 hours/week of manual work and reducing input errors to near zero.

Next

A great resume earns you the technical screen — now you have to pass it. Next we drill the queries and coding problems that gate most data roles. → Interview Prep I — SQL & Coding